Tools
Tools
CONTI, Daniela, DI NUOVO, Santo, CANGELOSI, Angelo and DI NUOVO, Alessandro (2016). Lateral specialization in unilateral spatial neglect : a cognitive robotics model. Cognitive Processing, 17 (3), 321-328.
|
PDF
art%3A10.1007%2Fs10339-016-0761-x.pdf - Published Version Creative Commons Attribution. Download (831kB) | Preview |
Abstract
In this paper, we present the experimental results of an embodied cognitive robotic approach for modelling the human cognitive deficit known as unilateral spatial neglect (USN). To this end, we introduce an artificial neural network architecture designed and trained to control the spatial attentional focus of the iCub robotic platform. Like the human brain, the architecture is divided into two hemispheres and it incorporates bio-inspired plasticity mechanisms, which allow the development of the phenomenon of the specialization of the right hemisphere for spatial attention. In this study, we validate the model by replicating a previous experiment with human patients affected by the USN and numerical results show that the robot mimics the behaviours previously exhibited by humans. We also simulated recovery after the damage to compare the performance of each of the two hemispheres as additional validation of the model. Finally, we highlight some possible advantages of modelling cognitive dysfunctions of the human brain by means of robotic platforms, which can supplement traditional approaches for studying spatial impairments in humans.
| Item Type: | Article |
|---|---|
| Research Institute, Centre or Group - Does NOT include content added after October 2018: | Cultural Communication and Computing Research Institute > Communication and Computing Research Centre |
| Departments - Does NOT include content added after October 2018: | Faculty of Science, Technology and Arts > Department of Computing |
| Identification Number: | https://doi.org/10.1007/s10339-016-0761-x |
| Page Range: | 321-328 |
| Depositing User: | Alessandro Di Nuovo |
| Date Deposited: | 06 Sep 2016 08:47 |
| Last Modified: | 18 Mar 2021 04:02 |
| URI: | https://shura.shu.ac.uk/id/eprint/12277 |
Actions (login required)
 |
View Item |
Downloads
Downloads per month over past year